Given the clusters of regulating targets and regulated response genes, we want to examine to what extent these are changes in expression levels of particular isoforms.
library(magrittr)
library(tidyverse)
library(pheatmap)
library(SummarizedExperiment)
set.seed(20210818)
## input files
FILE_WUI="tbl/df_wui_kd6_essential_ui10.Rds"
CSV_TARGETS="tbl/df_target_nnclusters_kd6_essential_ui10.csv"
CSV_GENES="tbl/df_gene_nnclusters_kd6_essential_ui10.csv"
NULL_CLUSTERS=as.character(c(9,12,13))
idx_clusters_targets <- as.character(c(14,18,11,16,13,9,12,2,5,15,1,6,8,3,7,4,10,17))
idx_clusters_genes <- as.character(c(20,16,7,8,1,3,18,2,5,10,15,19,14,11,4,6,17))
## output files
FILE_OUT_SU_FINAL="img/heatmap-su-kd6-clusters.pdf"
FILE_OUT_LU_FINAL="img/heatmap-lu-kd6-clusters.pdf"
## aesthetics
NCOLORS=100
COLORS_BWR <- colorRampPalette(c("blue", "white", "red"))(NCOLORS)
COLORS_MKY <- colorRampPalette(c("magenta", "black", "yellow"))(NCOLORS)
COLORS_YKM <- colorRampPalette(c("yellow", "black", "magenta"))(NCOLORS)
COLORS_RKG <- colorRampPalette(c("red", "black", "green"))(NCOLORS)
breaks_l2fc <- seq(-2, 2, length.out=NCOLORS + 1)
df_targets <- read_csv(CSV_TARGETS, col_types='cccc')
df_genes <- read_csv(CSV_GENES, col_types='ccc')
df_wui <- readRDS(FILE_WUI)
sgid2gene <- df_wui %>%
dplyr::select(sgID_AB, target_gene) %>%
distinct(sgID_AB, target_gene) %>%
deframe()
ens2gene <- df_wui %>%
dplyr::select(gene_id, gene_name) %>%
distinct(gene_id, gene_name) %>%
deframe()
convert_rownames <- function (mat, in2out) {
mat %>% set_rownames(in2out[rownames(.)])
}
convert_colnames <- function (mat, in2out) {
mat %>% set_colnames(in2out[colnames(.)])
}
df_ntp <- filter(df_wui,
target_gene == "non-targeting",
gene_id %in% df_genes$gene_id) %>%
group_by(gene_id, gene_name) %>%
filter(!is.na(wui)) %>%
summarize(mean_tpm_su=weighted.mean(tpm_su, n_cells),
sd_tpm_su=sqrt(sum((tpm_su-mean_tpm_su)^2)/n()),
mean_tpm_lu=weighted.mean(tpm_lu, n_cells),
sd_tpm_lu=sqrt(sum((tpm_lu-mean_tpm_lu)^2)/n()),
mean_wui=weighted.mean(wui, n_cells),
.groups='drop')
df_l2fc <- df_wui %>%
filter(sgID_AB %in% df_targets$sgID_AB,
gene_id %in% df_genes$gene_id) %>%
inner_join(df_ntp, by=c("gene_id", "gene_name")) %>%
mutate(l2fc_su=log2(tpm_su/mean_tpm_su),
l2fc_lu=log2(tpm_lu/mean_tpm_lu)) %>%
dplyr::select(gene_id, sgID_AB, tpm_su, l2fc_su, tpm_lu, l2fc_lu, mean_wui)
l2fc_su_target_gene <- df_l2fc %>%
dplyr::select(gene_id, sgID_AB, l2fc_su) %>%
pivot_wider(id_cols="sgID_AB", names_from="gene_id", values_from="l2fc_su") %>%
column_to_rownames("sgID_AB") %>%
as.matrix
l2fc_lu_target_gene <- df_l2fc %>%
dplyr::select(gene_id, sgID_AB, l2fc_lu) %>%
pivot_wider(id_cols="sgID_AB", names_from="gene_id", values_from="l2fc_lu") %>%
column_to_rownames("sgID_AB") %>%
as.matrix
idx_genes <- df_genes %>%
filter(gene_id %in% colnames(l2fc_su_target_gene),
cluster_id %in% idx_clusters_genes) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
arrange(cluster_id, gene_name) %$%
gene_id
idx_genes_null <- df_genes %>%
filter(gene_id %in% colnames(l2fc_su_target_gene),
cluster_id %in% NULL_CLUSTERS) %>%
mutate(cluster_id=factor(cluster_id, levels=NULL_CLUSTERS)) %>%
arrange(cluster_id, gene_name) %$%
gene_id
idx_targets <- df_targets %>%
filter(sgID_AB %in% rownames(l2fc_su_target_gene)) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
arrange(cluster_id, target_gene) %$%
sgID_AB
df_col_annots <- df_genes %>%
filter(gene_id %in% colnames(l2fc_su_target_gene),
cluster_id %in% idx_clusters_genes) %>%
dplyr::select(gene_id, cluster_id) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
dplyr::rename(gene_cluster=cluster_id) %>%
column_to_rownames("gene_id")
df_row_annots <- df_targets %>%
filter(sgID_AB %in% rownames(l2fc_su_target_gene)) %>%
dplyr::select(sgID_AB, cluster_id) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
dplyr::rename(target_cluster=cluster_id) %>%
column_to_rownames("sgID_AB")
gaps_col <- df_genes %>%
filter(gene_id %in% colnames(l2fc_su_target_gene),
cluster_id %in% idx_clusters_genes) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
arrange(cluster_id, gene_id) %$%
table(cluster_id) %>%
cumsum
gaps_row <- df_targets %>%
filter(sgID_AB %in% rownames(l2fc_su_target_gene)) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
arrange(cluster_id, sgID_AB) %$%
table(cluster_id) %>%
cumsum
pheatmap(l2fc_su_target_gene[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_l2fc,
fontsize_col=1, fontsize_row=10,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE)
pheatmap(l2fc_lu_target_gene[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_l2fc,
fontsize_col=1, fontsize_row=10,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE)
pheatmap(l2fc_su_target_gene[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_l2fc,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=TRUE, show_rownames=TRUE, scale='none',
labels_row=sgid2gene[idx_targets],
labels_col=ens2gene[idx_genes],
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE,
filename=FILE_OUT_SU_FINAL, width=16, height=16)
pheatmap(l2fc_lu_target_gene[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_l2fc,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=TRUE, show_rownames=TRUE, scale='none',
labels_row=sgid2gene[idx_targets],
labels_col=ens2gene[idx_genes],
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE,
filename=FILE_OUT_LU_FINAL, width=16, height=16)
df_l2fc %>%
filter(str_detect(sgID_AB, "NUDT21")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="NUDT21") +
theme_bw()
df_l2fc %>%
filter(str_detect(sgID_AB, "CPSF6")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="CPSF6") +
theme_bw()
df_l2fc %>%
filter(str_detect(sgID_AB, "(NUDT21|CPSF6)")) %>%
left_join(df_targets, by="sgID_AB") %>%
dplyr::select(gene_id, target_gene, l2fc_su, l2fc_lu, mean_wui) %>%
pivot_wider(id_cols=c("gene_id", "mean_wui"), names_from="target_gene",
values_from=c("l2fc_su", "l2fc_lu")) %>%
ggplot(aes(x=l2fc_su_NUDT21, y=l2fc_su_CPSF6)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(color=mean_wui), size=1) +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="NUDT21 [log2(fold-change SU)]",
y="CPSF6 [log2(fold-change SU)]",
color="Mean WUI") +
theme_bw()
df_l2fc %>%
filter(str_detect(sgID_AB, "(NUDT21|CPSF6)")) %>%
left_join(df_targets, by="sgID_AB") %>%
dplyr::select(gene_id, target_gene, l2fc_su, l2fc_lu, mean_wui) %>%
pivot_wider(id_cols=c("gene_id", "mean_wui"), names_from="target_gene",
values_from=c("l2fc_su", "l2fc_lu")) %>%
ggplot(aes(x=l2fc_lu_NUDT21, y=l2fc_lu_CPSF6)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(color=mean_wui), size=1) +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="NUDT21 [log2(fold-change LU)]",
y="CPSF6 [log2(fold-change LU)]",
color="Mean WUI") +
theme_bw()
What is that gene in the top right?
df_l2fc %>%
filter(str_detect(sgID_AB, "(NUDT21|CPSF6)")) %>%
left_join(df_targets, by="sgID_AB") %>%
filter(l2fc_lu > 2) %>%
mutate(gene_name=ens2gene[as.character(gene_id)])
## # A tibble: 5 × 11
## gene_id sgID_AB tpm_su l2fc_su tpm_lu l2fc_lu mean_wui target_gene
## <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 ENSG00000116580.20 CPSF6_+… 7.68 0.286 3.84 2.10 0.127 CPSF6
## 2 ENSG00000163131.12 NUDT21_… 5.10 1.85 7.65 2.48 0.509 NUDT21
## 3 ENSG00000173598.14 CPSF6_+… 33.7 0.280 171. 2.92 0.357 CPSF6
## 4 ENSG00000173598.14 NUDT21_… 42.1 0.600 135. 2.57 0.357 NUDT21
## 5 ENSG00000182093.16 NUDT21_… 3.83 1.45 2.55 2.42 0.236 NUDT21
## # … with 3 more variables: target_gene_id <chr>, cluster_id <chr>,
## # gene_name <fct>
Nudt4. This turns out to really be more of the SU isoform, so not as strange as this plot might indicate. The actual LU isoforms fell below the 10% usage minimum.
df_l2fc %>%
filter(str_detect(sgID_AB, "PAF1")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="PAF1") +
theme_bw()
df_l2fc %>%
filter(str_detect(sgID_AB, "RTF1")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="RTF1") +
theme_bw()
df_l2fc %>%
filter(str_detect(sgID_AB, "CTR9")) %>%
ggplot(aes(x=l2fc_su, y=l2fc_lu)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_vline(xintercept=0, color='grey80') +
geom_vline(xintercept=c(-1,1), linetype='dashed') +
geom_point(aes(size=tpm_su+tpm_lu+0.1, color=mean_wui)) +
scale_size_continuous(range=c(0.01,1), trans="log10") +
scale_color_viridis_b(breaks=c(0,0.33,0.67,1), option="B") +
labs(x="SU [log2(fold-change)]",
y="LU [log2(fold-change)]",
size="TPM [SU+LU]", color="Mean WUI",
title="CTR9") +
theme_bw()
## Violin Plots ### PAF1 Complex
df_l2fc %>%
filter(str_detect(sgID_AB, "(CTR9|RTF1|PAF1)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
df_l2fc %>%
filter(str_detect(sgID_AB, "(NUDT21|CPSF6|OGFOD1)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning: Removed 5 rows containing non-finite values (stat_ydensity).
df_l2fc %>%
filter(str_detect(sgID_AB, "(CPSF1|CPSF2|CPSF3|CPSF4|WDR33|FIP1L1)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
df_l2fc %>%
filter(str_detect(sgID_AB, "(PCF11|CLP1)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
df_l2fc %>%
filter(str_detect(sgID_AB, "(NXF1|THOC)")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
df_l2fc %>%
filter(str_detect(sgID_AB, "SRSF")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
df_l2fc %>%
filter(str_detect(sgID_AB, "CSTF")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
df_l2fc %>%
filter(str_detect(sgID_AB, "EXOSC")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning: Removed 1 rows containing non-finite values (stat_ydensity).
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
df_l2fc %>%
filter(str_detect(sgID_AB, "PSM[ABCD]")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning: Removed 1 rows containing non-finite values (stat_ydensity).
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
df_l2fc %>%
filter(str_detect(sgID_AB, "LSM[0-9]")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
df_l2fc %>%
filter(str_detect(sgID_AB, "^RPL")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
df_l2fc %>%
filter(str_detect(sgID_AB, "^RPS")) %>%
mutate(target_gene=sgid2gene[sgID_AB],
mean_wui_bin=cut(mean_wui, breaks=c(0,0.33,0.67,1.0)),
l2fc_su=ifelse(l2fc_su < -4, -4, l2fc_su),
l2fc_lu=ifelse(l2fc_lu < -4, -4, l2fc_lu)) %>%
dplyr::select(target_gene, gene_id, l2fc_su, l2fc_lu, mean_wui_bin) %>%
pivot_longer(cols=c("l2fc_su", "l2fc_lu"),
names_prefix="l2fc_", names_to="isoform",
values_to="l2fc") %>%
mutate(isoform=factor(toupper(isoform), c("SU", "LU"))) %>%
ggplot(aes(x=target_gene, y=l2fc)) +
geom_hline(yintercept=0, color='grey80') +
geom_hline(yintercept=c(-1,1), linetype='dashed') +
geom_violin(aes(fill=mean_wui_bin), position="dodge", draw_quantiles=c(0.25, 0.5, 0.75)) +
scale_fill_viridis_d(option="B", begin=0.4, end=0.9) +
scale_y_continuous(limits=c(-4,4)) +
labs(x="Perturbation",
y="log2(fold-change)",
fill="Mean WUI") +
facet_wrap(vars(isoform), nrow=2) +
theme_bw()
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [3] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [5] IRanges_2.28.0 S4Vectors_0.32.0
## [7] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [9] matrixStats_0.61.0 pheatmap_1.0.12
## [11] forcats_0.5.1 stringr_1.4.0
## [13] dplyr_1.0.8 purrr_0.3.4
## [15] readr_2.1.1 tidyr_1.1.4
## [17] tibble_3.1.7 ggplot2_3.3.5
## [19] tidyverse_1.3.1 magrittr_2.0.3
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 fs_1.5.2 lubridate_1.8.0
## [4] bit64_4.0.5 RColorBrewer_1.1-2 httr_1.4.2
## [7] tools_4.1.1 backports_1.4.0 bslib_0.3.1
## [10] utf8_1.2.2 R6_2.5.1 DBI_1.1.1
## [13] colorspace_2.0-2 withr_2.4.3 tidyselect_1.1.1
## [16] bit_4.0.4 compiler_4.1.1 cli_3.3.0
## [19] rvest_1.0.2 xml2_1.3.3 DelayedArray_0.20.0
## [22] labeling_0.4.2 sass_0.4.0 scales_1.1.1
## [25] digest_0.6.29 rmarkdown_2.11 XVector_0.34.0
## [28] pkgconfig_2.0.3 htmltools_0.5.2 dbplyr_2.1.1
## [31] fastmap_1.1.0 highr_0.9 rlang_1.0.2
## [34] readxl_1.3.1 rstudioapi_0.13 jquerylib_0.1.4
## [37] generics_0.1.1 farver_2.1.0 jsonlite_1.7.2
## [40] vroom_1.5.7 RCurl_1.98-1.5 GenomeInfoDbData_1.2.7
## [43] Matrix_1.3-4 Rcpp_1.0.7 munsell_0.5.0
## [46] fansi_0.5.0 lifecycle_1.0.1 stringi_1.7.6
## [49] yaml_2.2.1 zlibbioc_1.40.0 grid_4.1.1
## [52] parallel_4.1.1 crayon_1.4.2 lattice_0.20-45
## [55] haven_2.4.3 hms_1.1.1 knitr_1.39
## [58] pillar_1.7.0 reprex_2.0.1 glue_1.6.2
## [61] evaluate_0.15 modelr_0.1.8 vctrs_0.4.1
## [64] tzdb_0.2.0 cellranger_1.1.0 gtable_0.3.0
## [67] assertthat_0.2.1 xfun_0.30 broom_0.8.0
## [70] viridisLite_0.4.0 ellipsis_0.3.2
## Conda Environment YAML
name: base
channels:
- conda-forge
- bioconda
- defaults
dependencies:
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- pyasn1=0.4.8=py_0
- pybind11-abi=4=hd8ed1ab_3
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- python_abi=3.9=2_cp39
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- tornado=6.2=py39h701faf5_0
- tqdm=4.62.2=pyhd8ed1ab_0
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- tzdata=2021e=he74cb21_0
- tzlocal=4.2=py39h6e9494a_1
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- wcwidth=0.2.5=pyh9f0ad1d_2
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- wheel=0.37.0=pyhd8ed1ab_1
- wrapt=1.14.1=py39h701faf5_0
- xxhash=0.8.0=h35c211d_3
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- zlib=1.2.13=hfd90126_4
- zstd=1.5.2=hfa58983_4
- pip:
- pyopenssl==20.0.1
prefix: /Users/mfansler/miniconda3